1. Technical Field
The present invention relates to characteristic-based profiling systems and, more particularly, to combining multiple points of data regarding individuals through the use of characteristics in order to determine the relationship between the individuals and a user-defined criteria.
2. Description of the Related Art
Customer profiling systems are known in the art. Traditional systems include consumer rewards cards, credit card purchase information, demographic profiling, behavioral profiling, and customer surveying. Some businesses supplement these traditional systems with website and social media analytic tools that profile the business's fans and followers according to factors such as “likes,” “click-through rates,” and search engine queries, among others. Generally, these systems attempt to determine products, promotions, and advertisements that are most likely to appeal to a specific customer or broad customer segment. This information helps businesses forecast future market behavior, manage their product portfolio and inventory levels, adjust product pricing, design marketing strategies, and determine human resource and capital investment needs in order to increase revenue, market share, and profitability. For example, advertising targeted at customers who are most likely to purchase a product may be more effective than advertising targeting broader audiences. Likewise, products that are related to one another are likely to be purchased by the same customer and may sell better if offered at the same time, whether as a package or as separate items. Online retailers often use a similar approach, suggesting items that other customers frequently purchase in conjunction with the selected item.
While most approaches create basic customer profiles, these profiles do not reflect the myriad similarities between customers or the numerous ways in which customers can be grouped. For example, most approaches generally provide profiles on either an individual customer or an overly broad customer segment (for example, all women ages 25-34 with a college degree), failing to reflect the various degrees of granularity with which customers can be grouped. One type of approach typically uses only historical, static, and quantitative or objective information. As a result, customer profiles created by these approaches are generally outdated and inaccurate, and fail to account for the vast amount of potentially rich, but qualitative and subjective, information about the customer that is available to most businesses. A second type of approach uses only subjective or qualitative information. These approaches also have drawbacks. Typically they use expensive and time-consuming methods such as customer surveys or focus groups. Due to the nature of the setting, the results may not accurately reflect the attitudes or opinions of the surveyed individuals. Due to the expense and time involved, only a limited number of individuals may be surveyed. Likewise, the purchasing suggestions created by these systems are often inaccurate. For example, while many customers who purchase item A also purchase item B, that information does not provide any insight into what a specific customer, or group of customers, may be interested in.
Additionally, customer information is often collected with respect to a single business metric and may never be used to glean insights about other metrics that may be helpful to the company. This is particularly true for businesses that are growing and those that have multiple departments. Growing businesses must usually adjust or supplement its performance metrics to reflect new goals, strategies, and business operations. As a result, these businesses must understand how its customers relate to the new set of business metrics rather than, or in addition to, the ones for which the data was originally collected. Similarly, businesses with multiple departments frequently gather customer information for purposes of a department-specific metric, but fail to use that information across other departments or globally within the organization. For example, a business may have a marketing department and risk management department. Customer information gathered by the marketing department when researching new product markets may never be seen or used by the risk management team to determine whether that customer or market poses undue risk to the business. Methods for combining this disparate data, (for example, a technique sometimes referred to as “one version of the truth analysis”) do not allow the business to apply the same method to external data it may be interested in. Furthermore, these systems are used only to organize the information and are not useful for analyzing it.
In the advertising, marketing, and public relations fields, businesses often tailor their communications with existing or potential consumers, investors, employees and others. These communications are tailored, to the greatest extent possible, for the business' purpose in sending the message and for the individual(s) who are intended to receive the message. Tailoring the message appropriately has become increasingly more difficult with the advent of real-time messaging, targeted advertising, social media, viral campaigns, and more. These communication platforms require businesses to communicate with the intended recipient as if on a 1-to-1 basis. Nevertheless, tailoring the message appropriately is only becoming more important as doing so often increases conversion rates, click-through rates, sales, and other barometers of an advertising, marketing or PR campaign's success. As discussed above, systems that use basic profiling techniques result in customer profiles that are generally outdated and inaccurate. As a result, these systems do not allow these businesses to tailor their message as narrowly as possible for each individual, or group.
For example, “cookies” are used to track and monitor user behavior on the Internet in order to measure the effectiveness of an advertisement, or other message. These cookies are ever more important in the Social Media context because users of such platforms frequently share content with other friends or people on the network, rather than actually clicking on the advertisement. Thus, it is increasingly important to understand the type of person who is accessing or viewing an advertisement or message; otherwise the value of such “word of mouth” marketing is greatly diminished. However, it is difficult to capture a complete profile of each individual or group of individuals through the use of cookies. Thus, there is a need for a system that can determine how effective an advertisement or message is, or will be, in reaching certain individuals or types of individuals.
In addition, many organizations attempt to collect real-time information about their customers or audience. However, this is often difficult to do with the basic profiling techniques described above. In addition, these basic techniques often cannot be performed in real time, and result in customer profiles that are generally outdated and inaccurate.
Many organizations also sell the data they collect about individuals to other organizations. Often however, this information is not very detailed. Many organizations will not (or cannot) sell sensitive or detailed information about each individual, due to privacy concerns and laws. Thus, there is a need for a system that is capable of creating a detailed profile about an individual, or group of individuals, in a way that does not reveal any identifying or sensitive information about the individual(s).
There is also a need for a system that can create a predictive profile of an individual, or group of individuals, in order to predict future behavior and performance. Many organizations attempt to track the performance of individuals. For example, large companies attempt to track employee performance, to determine which employees perform well, and which are likely to perform well in the future. These companies also attempt to determine how well each job candidate is likely to perform, if they were given a job with the company. Thus, there is a need for a system that can create a detailed profile about an individual or group of individuals, and determine how that profile relates to certain performance characteristics. There is also a need for a system that can provide this information without revealing sensitive information, or information the employer is not allowed to consider as a factor, about each individual,
In business environments where team projects and collaboration play a significant role, many companies struggle to assemble the most effective teams. Often, managers and human resource departments assemble teams on the basis of employees' skill sets, seniority, and/or experience. However, factors such as shyness, social dominance, creativity, leadership, the ability to officiate and foster an open and collaborative work environment, among many others, are not formally taken into consideration when assembling a team. Even when a business recognizes the importance of these and other subjective factors, they are not formally included in the analytical process because such factors are expensive and obtrusive to measure, based upon inconsistent or inaccurate anecdotes, or are measured through historical performance and peer assessment reviews. These basic systems do not provide real time feedback to team members regarding its performance with respect to critical subjective criteria, such as identifying overly-dominant team members who are drowning out others, identifying “free-riders”, identifying harmful “intra-group cliques”, identifying power-struggles within a team, or identifying detrimental body language, tone-of-voice, or communication habits. Thus, the basic systems do not enable a business to address these important group dynamic issues until after it is too late.
Characteristic based screening can also be used to identify which individuals have a greater propensity to be a terrorist, sex-offender, or spy. Basic systems rely on identifying known associations between an individual and other potentially harmful or threatening people or organizations. These associations, however, do not enable a government agency to measure “grey areas” of a person. For example, two individuals may be nearly identical in their known associations, but one person is often angry, vengeful, and has an explosive temperament. In contrast, the second individual may be characterized by spirituality, self-evaluation, and ambition. As a result, the first individual may have a greater correlation with anti-social behavior and should not be trusted.
One of the most difficult aspects facing businesses today is how to measure the value of its connections and affiliations on social media platforms. Specifically, there is a need to understand and measure characteristics such as an affiliated individual's reach, the strength of the connection between the individual and the business, the strength of the connection between the individual and his or her personal social network and sub-networks, and the trustworthiness of the connection with the individual, the density of the business' social network and that of its “fans”, “followers”, and “likes.” This would enable a business to measure its connections, understanding which connections reach the most people, have the greatest impact on people (and which people), have the greatest ability to motivate action in others, and are interested in topics affiliated or connected, either directly or indirectly, with the business. All of this may then be correlated back to the business' own metrics, so that it may know who is a valuable social media connection, when, and for what reason.
As a result, there is a need for a system that addresses the issues above.